Estimation of Lower Limb Periodic Motions from sEMG Using Least Squares Support Vector Regression
Li, Q. L.; Song, Y.; Hou, Z. G.; Z. G. Hou
2015
发表期刊NEURAL PROCESSING LETTERS
期号3, SI页码:371-388
摘要In this paper, a new technique for predicting human lower limb periodic motions
from multi-channel surface ElectroMyoGram (sEMG) was proposed on the basis of leastsquares support vector regression (LS-SVR). The sEMG signals were sampled from seven
human lower limb muscles. Two channels sEMG were selected and mapped to muscle activation levels for angles estimation based on cross-correlation analysis. To deal with the time
delay introduced by low-pass filtering of raw sEMG, a
k-order dynamic model was derived
to represent the dynamic relationship between the joint angles and muscle activation levels.
The dynamic model was built by data driven LS-SVR with radial basis function kernel. The
inputs of the LS-SVR are muscle activation levels, and the outputs are joint angles of the hip
and knee. In experiments, 48 sEMG-angle datasets sampled from six healthy people were
utilized to verify the effectiveness of the proposed method. Result shows that the human
lower limb joint angles can be well estimated in different motion conditions.

关键词Semg Ls-svr Motion Estimation Neural Network
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/19946
专题复杂系统管理与控制国家重点实验室_先进机器人
通讯作者Z. G. Hou
推荐引用方式
GB/T 7714
Li, Q. L.,Song, Y.,Hou, Z. G.,et al. Estimation of Lower Limb Periodic Motions from sEMG Using Least Squares Support Vector Regression[J]. NEURAL PROCESSING LETTERS,2015(3, SI):371-388.
APA Li, Q. L.,Song, Y.,Hou, Z. G.,&Z. G. Hou.(2015).Estimation of Lower Limb Periodic Motions from sEMG Using Least Squares Support Vector Regression.NEURAL PROCESSING LETTERS(3, SI),371-388.
MLA Li, Q. L.,et al."Estimation of Lower Limb Periodic Motions from sEMG Using Least Squares Support Vector Regression".NEURAL PROCESSING LETTERS .3, SI(2015):371-388.
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